Masked label prediction
The UniMP model presented by [1]} aims to solve the graph node classification problem using graph transformer networks [2]}.
They maximized the propagation information required to reconstruct a partially observable label by using both feature information and label information as inputs. However, to prevent overfitting due to excessive information, some label information is masked, and the masked label is predicted. We apply a similar method to text data for an intermediate task with rationales. Through additional rationale information, the model increases the understanding of input sentences, and the performance of the downstream task is improved.
